What is Supervised Learning?

• Learning from labelled examples to predict outcomes on new data.
• You provide features (X), the input variables describing each observation, and the target (y), the outcome you want to predict.
• The model learns the relationship between X and y, and predicts the value of y given X.

Two Types of Supervised Learning

• Classification - Predicting categories
  • Will this patient be readmitted? (Yes/No)
  • Which diagnosis code applies? (A, B, C, or D)
  • Binary or multi-class outcomes
• Regression - Predicting continuous numbers
  • How long will this patient stay in hospital? (days)
  • What will next month’s A&E attendances be? (count)
  • Numerical outcomes

Classification vs Regression

Note - Some algorithms work for both (e.g., decision trees)

The Generalisation Challenge

• The goal of supervised learning is to learn patterns that work on new data, not just training data.
• But this is difficult. Models can underfit and overfit, and will not generalise well.
  • Underfitting - Model too simple, misses patterns, performs poorly on training and test data.
  • Overfitting - Model too complex, memorises training data, performs well on training but poorly on test data.
• The sweet spot is a model that captrues real patterns/signal but ignores noise.

The Learning Process

• Models don’t interpret or understand data like humans do.
• They find mathematical patterns that connect features to outcomes.
• Different algorithms find different types of patterns.
• There is no single “best” algorithm.
  • This is known as the No Free Lunch Theorem.

Thank You!

Contact:

Code & Slides:

/NHS-South-Central-and-West/code-club

… And don’t forget to give us your feedback.